Toward Optimal Risk-Averse Configuration for HESS With CGANs-Based PV Scenario Generation

In this paper, an optimal risk-averse configuration framework for hybrid energy storage system (HESS) is proposed in planning utility-scale photovoltaic (PV) plants with conditional generative adversarial networks (CGANs)-based PV scenario generation. Other than most existing economy-based methods, we focus on frequency-based method to size a battery-supercapacitor HESS for mitigating the PV generation fluctuations in two time scales. We explore for the first time the potential of CGANs to generate sufficient PV scenarios through learning for experimental data preparation. For satisfying the fluctuation restrictions strictly, a data-driven frequency-based batteries optimization is developed, combining the flexible low-pass filter with wavelet package transform to guide the behaviors of both battery and supercapacitor for every individual scenario. Moreover, to hedge against risk exposure imposed by uncertain PV resource, we employ conditional value-at-risk to perform the optimal risk-averse configuration to meet the fluctuation mitigating requirements and minimize the expected configuration as well as the risk. Case studies are provided to verify the reasonableness and the efficiency of the proposed method.

Automated summary

This paper proposes an optimal risk-averse configuration framework for hybrid energy storage system (HESS) in planning utility-scale photovoltaic (PV) plants using conditional generative adversarial networks (CGANs) for PV scenario generation. The method focuses on battery-supercapacitor sizing based on frequency to mitigate PV generation fluctuations, while employing data-driven frequency-based batteries optimization and conditional value-at-risk to hedge against uncertain PV resource risk exposure. Case studies are provided to verify the reasonableness and efficiency of the proposed method.